SoroushVahidi/scidocs huggingface dataset:
Projection Based Weight Normalization for Deep Neural Networks
(2017)

Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem. We observe that the scaling-based weight space symmetry property in rectified nonlinear network will cause this negative effect. Therefore, we propose to constrain the incoming weights of each neuron to be unit-norm, which is formulated as an optimization problem over Oblique manifold. A simple yet efficient method referred to as projection based weight normalization (PBWN) is also developed to solve this problem. PBWN executes standard gradient updates, followed by projecting the updated weight back to Oblique manifold. This proposed method has the property of regularization and collaborates well with the commonly used batch normalization technique. We conduct comprehensive experiments on several widely-used image datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art convolutional neural networks, such as Inception, VGG and residual networks. The results show that our method is able to improve the performance of DNNs with different architectures consistently. We also apply our method to Ladder network for semi-supervised learning on permutation invariant MNIST dataset, and our method outperforms the state-of-the-art methods: we obtain test errors as 2.52%, 1.06%, and 0.91% with only 20, 50, and 100 labeled samples, respectively.
Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora
(2010)

We propose a semi-supervised model which segments and annotates images using very few labeled images and a large unaligned text corpus to relate image regions to text labels. Given photos of a sports event, all that is necessary to provide a pixel-level labeling of objects and background is a set of newspaper articles about this sport and one to five labeled images. Our model is motivated by the observation that words in text corpora share certain context and feature similarities with visual objects. We describe images using visual words, a new region-based representation. The proposed model is based on kernelized canonical correlation analysis which finds a mapping between visual and textual words by projecting them into a latent meaning space. Kernels are derived from context and adjective features inside the respective visual and textual domains. We apply our method to a challenging dataset and rely on articles of the New York Times for textual features. Our model outperforms the state-of-the-art in annotation. In segmentation it compares favorably with other methods that use significantly more labeled training data.
Reducing Patient Mortality in Hospitals : The Role of Human Resource
(2006)

Developing effective health care organizations is increasingly complex as a result of demographic changes, globalization and developments in medicine. This study examines the potential contribution of organizational behavior theory and research by investigating the relationship between systems of human resource management (HRM) practices and effectiveness of patient care in hospitals. Relatively little research has been conducted to explore these issues in health care settings. In a sample of 52 hospitals in England, we examine the relationship between the HRM system and health care outcome. Specifically, we study the association between high performance HRM policies and practices and standardized patient mortality rates. The research reveals that, after controlling for prior mortality and other potentially confounding factors such as the ratio of doctors to patients, greater use of a complementary set of HRM practices has a statistically and practically significant relationship with patient mortality. The findings suggest that managers and policy makers should focus sharply on improving the functioning of relevant HR management systems in health care organizations as one important means by which to improve patient care.
Digital Calibration Techniques for Pipelined ADC ’ s
(1997)

A skip and fill algorithm is developed to digitally self-calibrate pipelined analog-to-digital converters (ADC’s) in real time. The proposed digital calibration technique is applicable to capacitor-ratioed multiplying digital-to-analog converters (MDAC’s) commonly used in multi-step or pipelined ADC’s. This background calibration process can replace, in effect, a trimming procedure usually done in the factory with a hidden electronic calibration. Unlike other self-calibration techniques working in the foreground, the proposed technique is based on the concept of skipping conversion cycles randomly but filling in data later by nonlinear interpolation. This opens up the feasibility of digitally implementing calibration hardware and simplifying the task of self-calibrating multi-step or pipelined ADC’s. The proposed method improves the performance of the inherently fast ADC’s by maintaining simple system architectures. To measure errors resulting from capacitor mismatch, op amp dc gain, offset, and switch feedthrough in real time, the calibration test signal is injected in place of the input signal using a split-reference injection technique. Ultimately, the missing signal within 2/3 of the Nyquist bandwidth is recovered with 16-bit accuracy using a 44-th order polynomial interpolation, behaving essentially as an FIR filter. This work was supported by the National Science Foundation under Grant MIP-9312671.
Role of Broca's Area in Implicit Motor Skill Learning: Evidence from Continuous Theta-burst Magnetic Stimulation
(2012)

Complex actions can be regarded as a concatenation of simple motor acts, arranged according to specific rules. Because the caudal part of the Broca's region (left Brodmann's area 44, BA 44) is involved in processing hierarchically organized behaviors, we aimed to test the hypothesis that this area may also play a role in learning structured motor sequences. To address this issue, we investigated the inhibitory effects of a continuous theta-burst TMS (cTBS) applied over left BA 44 in healthy subjects, just before they performed a serial RT task (SRTT). SRTT has been widely used to study motor skill learning and is also of interest because, for complex structured sequences, subjects spontaneously organize them into smaller subsequences, referred to as chunks. As a control, cTBS was applied over the vertex in another group, which underwent the same experiment. Control subjects showed both a general practice learning effect, evidenced by a progressive decrease in RT across blocks and a sequence-specific learning effect, demonstrated by a significant RT increase in a pseudorandom sequence. In contrast, when cTBS was applied over left BA 44, subjects lacked both the general practice and sequence-specific learning effects. However, surprisingly, their chunking pattern was preserved and remained indistinguishable from controls. The present study indicates that left BA 44 plays a role in motor sequence learning, but without being involved in elementary chunking. This dissociation between chunking and sequence learning could be explained if we postulate that left BA 44 intervenes in high hierarchical level processing, possibly to integrate elementary chunks together.
A 5.8-GHz radar sensor chip in 0.18-μm CMOS for non-contact vital sign detection
(2016)

This paper presents a 5.8-GHz radar sensor chip for non-contact vital sign detection. The sensor chip is designed and fabricated in TSMC 0.18 μm CMOS 1P6M process. Except for the low-noise amplifier, all the active and passive components of the radar system are fully integrated in a single CMOS chip. With packaging on the printed-circuit board and connecting transmitting and receiving antennas, the radar sensor chip has been successfully demonstrated to detect the respiration and heart beat rates of a human adult.
Linear scan register allocation
(1999)

We describe a new algorithm for fast global register allocation called linear scan. This algorithm is not based on graph coloring, but allocates registers to variables in a single linear-time scan of the variables' live ranges. The linear scan algorithm is considerably faster than algorithms based on graph coloring, is simple to implement, and results in code that is almost as efficient as that obtained using more complex and time-consuming register allocators based on graph coloring. The algorithm is of interest in applications where compile time is a concern, such as dynamic compilation systems, “just-in-time” compilers, and interactive development environments.
A Classification and Comparison Framework for Software Architecture Description Languages
(2000)

ÐSoftware architectures shift the focus of developers from lines-of-code to coarser-grained architectural elements and their overall interconnection structure. Architecture description languages (ADLs) have been proposed as modeling notations to support architecture-based development. There is, however, little consensus in the research community on what is an ADL, what aspects of an architecture should be modeled in an ADL, and which of several possible ADLs is best suited for a particular problem. Furthermore, the distinction is rarely made between ADLs on one hand and formal specification, module interconnection, simulation, and programming languages on the other. This paper attempts to provide an answer to these questions. It motivates and presents a definition and a classification framework for ADLs. The utility of the definition is demonstrated by using it to differentiate ADLs from other modeling notations. The framework is used to classify and compare several existing ADLs, enabling us, in the process, to identify key properties of ADLs. The comparison highlights areas where existing ADLs provide extensive support and those in which they are deficient, suggesting a research agenda for the future. Index TermsÐSoftware architecture, architecture description language, component, connector, configuration, definition, classification, comparison.
Specifying Distributed Software Architectures
(1995)

There is a real need for clear and sound design speci cations of distributed systems at the architectural level. This is the level of the design which deals with the high-level organisation of computational elements and the interactions between those elements. The paper presents the Darwin notation for specifying this high-level organisation. Darwin is in essence a declarative binding language which can be used to de ne hierarchic compositions of interconnected components. Distribution is dealt with orthogonally to system structuring. The language supports the speci cation of both static structures and dynamic structures which may evolve during execution. The central abstractions managed by Darwin are components and services. Services are the means by which components interact. In addition to its use in specifying the architecture of a distributed system, Darwin has an operational semantics for the elaboration of speci cations such that they may be used at runtime to direct the construction of the desired system. The paper describes the operational semantics of Darwin in terms of the -calculus, Milner's calculus of mobile processes. The correspondence between the treatment of names in the -calculus and the management of services in Darwin leads to an elegant and concise -calculus model of Darwin's operational semantics. The model is used to argue the correctness of the Darwin elaboration process. The overall objective is to provide a soundly based notation for specifying and constructing distributed software architectures. This paper will appear in the Fifth European Software Engineering Conference, ESEC '95 on 26 September 1995 in Barcelona.
ADLs and dynamic architecture changes
(1996)

Existing ADLs typically support only static architecture specification and do not provide facilities for the support of dynamically changing architectures. This paper presents a possible solution to this problem: in order to adequately support dynamic architecture changes, ADLs can leverage techniques used in dynamic programming languages. In particular, changes to ADL specifications should be interpreted. To enable interpretation, an ADL should have an architecture construction component that supports explicit and incremental specification of architectural changes, in addition to the traditional architecture description facilities. This will allow software architects to specify the changes to an architecture after it has been built. The paper expands upon the results from an ongoing project -building a development environment for CZstyle architectures.’
A two-stage short-term load forecasting approach using temperature daily profiles estimation
(2017)

Electrical load forecasting plays an important role in the regular planning of power systems, in which load is influenced by several factors that must be analysed and identified prior to modelling in order to ensure better and instant load balancing between supply and demand. This paper proposes a two-stage approach for short-term electricity load forecasting. In the first stage, a set of day classes of load profiles are identified using K-means clustering algorithm alongside daily temperature estimation profiles. The proposed estimation method is particularly useful in case of lack of historical regular temperature data. While in the second stage, the stacked denoising autoencoders approach is used to build regression models able to forecast each day type independently. The obtained models are trained and evaluated using hourly electricity power data offered by Algeria’s National Electricity and Gas Company. Several models are investigated to substantiate the accuracy and effectiveness of the proposed approach.
Performance analysis of DCO-OFDM in VLC system
(2015)

The performance of indoor visible light communication (VLC) systems using a direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) scheme is investigated in this paper. The impact of nonlinearity of Light Emitting Diode (LED) and its beam angle on the VLC system performance is studied. We later analyze the effect of modulation order, number of subcarriers, signal scaling and biasing operation on the peak to average power ratio (PAPR) which is a major issue in OFDM based VLC system. Simulation results show that the bit error rate (BER) decreases as the degree of nonlinearity increases and a better BER can be achieved as the LED behavior approaches linear model. In addition, it is shown that reducing the QAM order or increasing the number of subcarriers may reduce the effect of the LED nonlinearity, thus improving the BER performance of the VLC system. Moreover, it is demonstrated that PAPR is higher for a large number of subcarriers and modulation order. Finally, the PAPR of the visible light OFDM system can significantly be reduced by employing the signal scaling combined with biasing operation.
When Does More Money Work? Examining the Role of Perceived Fairness in Pay on the Performance Quality of Crowdworkers
(2017)

This research adds to the rich discussion on whether increases in payment to crowdworkers lead to increases in performance quality by introducing the concept of perceived fairness in pay (PFP). PFP refers to the belief that one is fairly compensated for their work. We examine whether PFP mediates the impact of payment amount on the performance quality of crowdworkers. We conducted a field experiment with 152 crowdworkers performing a buttonclicking (BC) task and an instructional manipulation check (IMC) task. PFP mediated the impact of payment amount on performance quality in the BC task but not in the IMC task. PFP also mediated the impact of payment amount on satisfaction and task time. Results suggest that PFP can help us better understand the relationship between payment and performance quality in crowdsourcing.
Efficient frame-sequential label propagation for video object segmentation
(2017)

In this work, we present an approach for segmenting objects in videos taken in complex scenes. It propagates initial object label through the entire video by a frame-sequential manner where the initial label is usually given by the user. The proposed method has several contributions which make the propagation much more robust and accurate than other methods. First, a novel supervised motion estimation algorithm is employed between each pair of neighboring frames, by which a predicted shape model can be warped in order to segment the similar color around object boundary. Second, unlike previous methods with fixed modeling range, we design a novel range-adaptive appearance model to handle the tough problem of occlusion. Last, the paper gives a reasonable framework based on GraphCut algorithm for obtaining the final label of the object by combining the clues from both appearance and motion. In the experiments, the proposed approach is evaluated qualitatively and quantitatively with some recent methods to show it achieves state-of-art results on multiple videos from benchmark data sets.
A combined transmembrane topology and signal peptide prediction method.
(2004)

An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes. In addition, topology information can be gained when successfully predicting a signal peptide leading a transmembrane protein since it dictates that the N terminus of the mature protein must be on the non-cytoplasmic side of the membrane. Here, we present Phobius, a combined transmembrane protein topology and signal peptide predictor. The predictor is based on a hidden Markov model (HMM) that models the different sequence regions of a signal peptide and the different regions of a transmembrane protein in a series of interconnected states. Training was done on a newly assembled and curated dataset. Compared to TMHMM and SignalP, errors coming from cross-prediction between transmembrane segments and signal peptides were reduced substantially by Phobius. False classifications of signal peptides were reduced from 26.1% to 3.9% and false classifications of transmembrane helices were reduced from 19.0% to 7.7%. Phobius was applied to the proteomes of Homo sapiens and Escherichia coli. Here we also noted a drastic reduction of false classifications compared to TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome annotation of signal peptides and transmembrane regions. The method is available at as well as at
Design of a triangular fractal patch antenna with slit for IRNSS and GAGAN applications
(2013)

In this paper a triangular fractal patch antenna with slit is designed for IRNSS and GAGAN applications using ADS software. India is intended to develop a satellite based navigation systems known as Indian Regional Navigational Satellite System (IRNSS) for positioning applications. Design of IRNSS antenna at user sector is indispensable. GPS Aided and Geo Augmented Navigation (GAGAN), a satellite based augmentation system for India, erect over the GPS system is anticipated to provide the flawless navigation support over the Asia-Pacific regions. The desired antenna has been deliberate on dielectric constant εr = 4.8 and substrate thickness h = 3.05 mm. The feed location of the antenna has been selected to produce the circular polarization. The self-similar property in antenna exhibits multi-band resonant frequencies. These specifications should be satisfied at the frequency L5 (1175 MHz), L1 (1575.42 MHz) and S (2492.08 MHz).
Flame detection in video using hidden Markov models
(2005)

This paper proposes a novel method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame flicker process is also detected by using a hidden Markov model. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish flame flicker process from motion of flame colored moving objects. Spatial color variations in flame are also evaluated by the same Markov models, as well. These clues are combined to reach a final decision. False alarms due to ordinary motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.
Sockets for Limb Prostheses: A Review of Existing Technologies and Open Challenges
(2018)

In the prosthetics field, one of the most important bottlenecks is still the human-machine interface, namely the socket. Indeed, a large number of amputees still rejects prostheses or points out a low satisfaction level, due to a sub-optimal interaction between the socket and the residual limb tissues. The aim of this paper is to describe the main parameters (displacements, stress, volume fluctuations and temperature) affecting the stump-socket interface and reducing the comfort/stability of limb prostheses. In this review, a classification of the different socket types proposed in the literature is reported, together with an analysis of advantages and disadvantages of the different solutions, from multiple viewpoints. The paper then describes the technological solutions available to face an altered distribution of stresses on the residual limb tissues, volume fluctuations affecting the stump overtime and temperature variations affecting the residual tissues within the socket. The open challenges in this research field are highlighted and the possible future routes are discussed, towards the ambitious objective of achieving an advanced socket able to self-adapt in real-time to the complex interplay of factors affecting the stump, during both static and dynamic tasks.
Visual inertial odometry for quadrotors on SE(3)
(2016)

The combination of on-board sensors measurements with different statistical characteristics can be employed in robotics for localization and control, especially in GPS-denied environments. In particular, most aerial vehicles are packaged with low cost sensors, important for aerial robotics, such as camera, a gyroscope, and an accelerometer. In this work, we develop a visual inertial odometry system based on the Unscented Kalman Filter (UKF) acting on the Lie group SE(3), such to obtain an unique, singularity-free representation of a rigid body pose. We model this pose with the Lie group SE(3) and model the noise on the corresponding Lie algebra. Moreover, we extend the concepts used in the standard UKF formulation, such as state uncertainty and modeling, to correctly incorporate elements that do not belong to an Euclidean space such as the Lie group members. In this analysis, we use the parallel transport, which requires us to explicitly consider SE(3) as representing rigid bodies though the use of the affine connection. We present experimental results to show the effectiveness of the proposed approach for state estimation of a quadrotor platform.
Sleep and autism spectrum disorders.
(2011)

Sleep disorders are common in children with autism spectrum disorders and have a significant effect on daytime function and parental stress. The cornerstone of treatment is to establish the cause of the sleep concern, which is often multifactorial. Identifying and treating sleep disorders may result not only in more consolidated sleep, more rapid time to fall asleep, and avoidance of night waking but also favorably affect daytime behavior and parental stress. Targeting effective treatment strategies is dependent on understanding the underlying causes of sleep problems in children with Autism spectrum disorders, therefore further research is paramount.